Building photovoltaic integrated operation and maintenance method and equipment based on industrial internet
Through the integrated operation and maintenance method of building photovoltaics based on the industrial Internet, data is obtained using sensors and photovoltaic inverters, feature extraction and correlation analysis are solved, and the problem of difficulty in monitoring operation and maintenance of distributed photovoltaic projects is achieved, and fault prediction and timely processing are achieved, and power generation efficiency and economic benefits are improved.
Patent Information
- Application Number
- CN202410079932.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-01-18
- Publication Date
- 2025-07-18
AI Technical Summary
Due to the dispersed location and small capacity of distributed photovoltaic projects, there are problems such as difficulty in monitoring and operation and maintenance, resulting in untimely detection of power station faults and inability to ensure power generation efficiency.
The integrated operation and maintenance method of building photovoltaics based on the industrial Internet is adopted, and the operating parameters of building roof equipment are obtained through preset sensors and the power generation parameters are obtained by obtaining the photovoltaic inverter, feature extraction and correlation analysis are performed, and faults and their impact are determined using graph neural network and covariance calculation to achieve intelligent monitoring and operation and maintenance all-weather.
It has achieved early prediction and timely handling of building photovoltaic failures, increased power generation, increased return on investment, and achieved good economic benefits.
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Figure CN120342319A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of industrial Internet, and particularly to a building photovoltaic integration operation and maintenance method and device based on the industrial Internet. Background Art
[0002] With the increasing severity of global warming, renewable energy represented by photovoltaic has received more and more attention. By combining photovoltaic modules with buildings, they can be directly installed on the rooftops of commercial buildings, industrial factories, and residential communities, etc., without occupying additional large land resources, which greatly improves the land utilization rate. Connecting the electricity generated by rooftop solar energy directly to the nearby power grid and consuming it locally not only reduces the transmission cost but also effectively helps people save electricity expenses.
[0003] In the prior art, due to characteristics such as scattered locations and small capacities of distributed photovoltaic projects, there are problems in monitoring and operation and maintenance. Currently, the operation and maintenance management work is mostly extensive management, relying more on manual on-site inspections, resulting in untimely discovery of power station failures and inability to guarantee power generation efficiency. Summary of the Invention
[0004] The embodiments of this application provide a building photovoltaic integration operation and maintenance method and device based on the industrial Internet to solve the following technical problems: Due to characteristics such as scattered locations and small capacities of distributed photovoltaic projects, there are problems in monitoring and operation and maintenance. Currently, the operation and maintenance management work is mostly extensive management, relying more on manual on-site inspections, resulting in untimely discovery of power station failures and inability to guarantee power generation efficiency.
[0005] The embodiments of this application adopt the following technical solutions:
[0006] The embodiments of this application provide a building photovoltaic integration operation and maintenance method based on the industrial Internet. The method includes obtaining the first operation parameters of the building rooftop equipment based on preset sensors, and obtaining the second operation parameters of photovoltaic power generation through a photovoltaic inverter; extracting features from the first operation parameters and the second operation parameters to obtain the first feature parameters that potentially affect the building rooftop equipment and the second feature parameters that potentially affect photovoltaic power generation; performing correlation analysis on the first feature parameters and the second feature parameters to determine the main components affecting photovoltaic power generation; obtaining the state analysis information corresponding to the building rooftop equipment based on a preset state analysis model and the first feature parameters, and determining the main components affecting the building rooftop equipment based on the state analysis information; performing operation and maintenance processing on the building rooftop equipment and photovoltaic power generation based on the main components affecting photovoltaic power generation and the main components affecting the building rooftop equipment.
[0007] The embodiment of the present application extracts features and performs correlation analysis on the collected roof operation data and photovoltaic power station operation data, and realizes all-weather intelligent monitoring, analysis and alarm, intelligent operation and maintenance and other applications based on data collection, analysis and processing technologies such as the Internet of Things, artificial intelligence, and data middle platform. It realizes early prediction and timely processing of building photovoltaic faults, thereby increasing power generation and increasing the return on investment to achieve good economic benefits.
[0008] In one implementation of the present application, a first characteristic parameter and a second characteristic parameter are subjected to a correlation analysis to determine the main component affecting photovoltaic power generation, specifically including: performing a correlation analysis on the first characteristic parameter and the second operating parameter by calculating the covariance; and performing a correlation analysis on the second characteristic parameter and the second operating parameter by calculating the covariance; based on the analysis results, determining the main component affecting photovoltaic power generation.
[0009] In one implementation of the present application, based on a preset state analysis model and a first characteristic parameter, state analysis information corresponding to building roof equipment is obtained, specifically including: constructing a graph neural network corresponding to preset sensors and building roof equipment; based on the graph neural network, determining associated faults that produce mutual influence relationships, and determining the weights between associated faults; wherein, the nodes in the graph neural network represent the faults corresponding to the building roof equipment, the edges in the graph neural network represent the relationship between the faults, the weights of the edges represent the degree of association between the faults, and the directions of the edges represent the mutual influence relationships between the faults. Based on the first characteristic parameter, the fault name is determined; based on the fault name, the associated faults, and the weights between the associated faults, the state analysis information corresponding to the building roof equipment is obtained.
[0010] In one implementation of the present application, status analysis information corresponding to the building roof equipment is obtained based on the fault name, associated faults and weights between the associated faults, specifically including: determining the corresponding alarm level based on the fault name; determining the fault level of the current fault and the associated faults related to the current fault respectively; determining the fault impact level based on the fault level; multiplying the fault impact level by the weight to determine the impact of the associated fault on the current fault based on the calculation result, and determining the status analysis information corresponding to the building roof equipment based on the impact level.
[0011] In an implementation manner of the present application, feature extraction is performed on the first operating parameter and the second operating parameter to obtain a first characteristic parameter that potentially affects the building roof equipment and a second characteristic parameter that potentially affects photovoltaic power generation. Specifically, it includes: classifying the first operating parameter and the second operating parameter; by means of principal component analysis, determining the average value corresponding to each type of operating parameter and determining the standard deviation corresponding to each type of operating parameter; constructing a standardization matrix based on the average value and standard deviation corresponding to each type of operating parameter; determining the correlation coefficient matrix corresponding to the standardization matrix, and obtaining a first characteristic parameter that potentially affects the building roof equipment and a second characteristic parameter that potentially affects photovoltaic power generation based on the correlation coefficient matrix, so as to determine the principal components that potentially affect the building roof equipment and potentially affect photovoltaic power generation based on the first characteristic parameter and the second characteristic parameter.
[0012] In an implementation manner of the present application, determining the correlation coefficient matrix corresponding to the standardization matrix specifically includes: decomposing the standardization matrix to obtain the eigenvalues and eigenvectors corresponding to each principal component; obtaining the correlation coefficient matrix based on the eigenvalues and eigenvectors; wherein, the eigenvector of the principal component is the entire set of variables composed of the principal component variables, and the eigenvalue of the principal component is the covariance between the principal component variables.
[0013] In an implementation manner of the present application, after obtaining the correlation coefficient matrix based on the eigenvalues and eigenvectors, the method further includes: performing diagonalization processing on the correlation coefficient matrix to obtain a diagonalized matrix; calculating new eigenvectors based on the diagonalized matrix, and using the new eigenvectors as new principal component variables, and determining the mapping relationships between the first operating parameter and the second operating parameter and the new principal component variables respectively.
[0014] In an implementation manner of the present application, after using the new eigenvectors as new principal component variables, the method further includes: sorting the new eigenvectors based on the magnitudes of the eigenvalues to determine the number of principal components to be extracted; extracting the new eigenvectors corresponding to the eigenvalues based on the number to serve as a standardized variable matrix; converting the standardized variable matrix to obtain principal component variables; and comprehensively evaluating the principal component variables to obtain the principal components that affect the building roof equipment and affect photovoltaic power generation.
[0015] In an implementation manner of the present application, among the first operating parameters of the building roof equipment obtained based on the preset sensors, the preset sensors include at least one of a vibration sensor, a water level sensor, a laser rangefinder, a stress sensor, a roof temperature sensor, and a meteorological sensor; the first operating parameters include at least one of vibration speed, water level height, deformation amount, roof stress, roof temperature, irradiance, and wind pressure and wind load.
[0016] An embodiment of the present application provides an industrial Internet-based building photovoltaic integrated operation and maintenance device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: obtain a first operating parameter of a building roof device based on a preset sensor, and obtain a second operating parameter of photovoltaic power generation through a photovoltaic inverter; perform feature extraction on the first operating parameter and the second operating parameter to obtain a first characteristic parameter that potentially affects the building roof device, and obtain a second characteristic parameter that potentially affects photovoltaic power generation; perform correlation analysis on the first characteristic parameter and the second characteristic parameter to determine a main component that affects photovoltaic power generation; obtain state analysis information corresponding to the building roof device based on a preset state analysis model and the first characteristic parameter, and determine the main component that affects the building roof device based on the state analysis information; and perform operation and maintenance processing on the building roof device and photovoltaic power generation based on the main component that affects photovoltaic power generation and the main component that affects the building roof device.
[0017] At least one of the above technical solutions adopted in the embodiment of the present application can achieve the following beneficial effects: The embodiment of the present application realizes all-weather intelligent monitoring, analysis and alarm, intelligent operation and maintenance and other applications by extracting features and analyzing the correlation of the collected roof operation data and photovoltaic power station operation data based on data collection, analysis and processing technologies such as the Internet of Things, artificial intelligence, and data middle platform. It realizes early prediction and timely processing of building photovoltaic faults, thereby increasing power generation and increasing the return on investment to achieve good economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. In the drawings:
[0019] Figure 1 A flow chart of a building photovoltaic integrated operation and maintenance method based on industrial Internet provided in an embodiment of the present application;
[0020] Figure 2 A schematic diagram of a graph neural network provided in an embodiment of the present application;
[0021] Figure 3 A structural schematic diagram of a building photovoltaic integrated operation and maintenance equipment based on the industrial Internet provided in an embodiment of the present application. DETAILED DESCRIPTION
[0022] An embodiment of the present application provides a method and device for building photovoltaic integration operation and maintenance based on the industrial Internet.
[0023] In order to enable those skilled in the art to better understand the technical solutions in the present application, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.
[0024] The following will detail the technical solutions proposed in the embodiments of the present application through the accompanying drawings.
[0025] Figure 1 It is a flowchart of a method for building photovoltaic integration operation and maintenance based on the industrial Internet provided for an embodiment of the present application. As Figure 1 shown, the method for building photovoltaic integration operation and maintenance based on the industrial Internet includes the following steps:
[0026] S101. Obtain the first operating parameters of the building roof equipment based on pre-set sensors, and obtain the second operating parameters of photovoltaic power generation through a photovoltaic inverter.
[0027] In an embodiment of the present application, among obtaining the first operating parameters of the building roof equipment based on pre-set sensors, the pre-set sensors include at least one of a vibration sensor, a water level sensor, a laser ranging sensor, a stress sensor, a roof temperature sensor, and a meteorological sensor. The first operating parameters include at least one of vibration speed, water level height, deformation amount, roof stress, roof temperature, irradiance, and wind force and wind pressure.
[0028] Specifically, sensors are deployed on the steel structure metal roof to collect the operating parameters of the building roof equipment in real time, and the power operating parameters of photovoltaic power generation are collected through a photovoltaic inverter. Six types of sensors, namely a vibration sensor, a water level sensor, a laser ranging sensor, a stress sensor, a roof temperature sensor, and a meteorological sensor, are deployed on the steel structure metal roof. Through the six types of deployed sensors, the operating parameters of the building roof equipment can be obtained. Among them, the operating parameters of the building roof equipment include at least operating parameters such as vibration speed, water level height, deformation amount, roof stress, roof temperature, irradiance, and wind force and wind pressure.
[0029] S102. Extract features from the first operating parameters and the second operating parameters to obtain the first feature parameters that potentially affect the building roof equipment, and obtain the second feature parameters that potentially affect photovoltaic power generation.
[0030] In an embodiment of the present application, the first operating parameter and the second operating parameter are classified. By means of principal component analysis, the average value corresponding to each type of operating parameter is determined, and the standard deviation corresponding to each type of operating parameter is determined. Based on the average value and the standard deviation corresponding to each type of operating parameter, a standardized matrix is constructed. The correlation coefficient matrix corresponding to the standardized matrix is determined, and a first characteristic parameter potentially affecting the building roof equipment and a second characteristic parameter potentially affecting the photovoltaic power generation are obtained, so as to determine the principal components potentially affecting the building roof equipment and the photovoltaic power generation based on the first characteristic parameter and the second characteristic parameter.
[0031] Specifically, in the way of principal component analysis, a standardized matrix of operating parameters is constructed. The correlation coefficient matrix is solved for the constructed standardized matrix of operating parameters to determine the principal components potentially affecting the building roof equipment and the photovoltaic power generation. Among them, the process of constructing the standardized matrix of operating parameters in the way of principal component analysis is as follows: the first operating parameter and the second operating parameter are classified, and the average value and the standard deviation of each type of operating parameter are calculated. Each type of operating parameter is subtracted by its average value and then divided by the standard deviation to construct a standardized matrix.
[0032] Furthermore, the correlation coefficient matrix corresponding to the standardized matrix is determined, and a first characteristic parameter potentially affecting the building roof equipment and a second characteristic parameter potentially affecting the photovoltaic power generation are obtained, so as to determine the principal components potentially affecting the building roof equipment and the photovoltaic power generation based on the first characteristic parameter and the second characteristic parameter.
[0033] In an embodiment of the present application, the standardized matrix is decomposed to obtain the eigenvalues and eigenvectors corresponding to each principal component. Based on the eigenvalues and eigenvectors, the correlation coefficient matrix is obtained. Among them, the eigenvector of the principal component is the entire set of variables composed of the principal component variables, and the eigenvalue of the principal component is the covariance between the principal component variables.
[0034] Specifically, the constructed standardized matrix of operating parameters is decomposed to form the eigenvalues and eigenvectors of each principal component. The eigenvector of the principal component is the entire set of variables composed of the principal component variables. The eigenvalue of the principal component is the covariance between the principal component variables, thereby forming the correlation coefficient matrix.
[0035] In an embodiment of the present application, the correlation coefficient matrix is diagonalized to obtain a diagonalized matrix. Based on the diagonalized matrix, new eigenvectors are calculated, and the new eigenvectors are used as new principal component variables, and the mapping relationships between the first operating parameter and the second operating parameter and the new principal component variables are determined.
[0036] Specifically, the correlation coefficient matrix is diagonalized to obtain the final diagonalized matrix. The diagonalized matrix is used to display the variance distribution and importance of the principal components. Using the diagonalized matrix, new eigenvectors are calculated as new principal component variables, thereby obtaining the mapping relationship from the operating parameters of the building roof equipment and the power operation parameters of photovoltaic power generation to the new principal component variables.
[0037] In one embodiment of the present application, based on the magnitudes of the eigenvalues, the new eigenvectors are sorted to determine the number of principal components to be extracted. Based on this number, the new eigenvectors corresponding to the eigenvalues are extracted to serve as the standardized variable matrix. The standardized variable matrix is transformed to obtain the principal component variables. A comprehensive evaluation is performed on the principal component variables to obtain the principal components that affect the building roof equipment and photovoltaic power generation.
[0038] Specifically, the new eigenvectors are sorted in descending order of eigenvalues to determine the number of principal components that can be extracted. Screening is performed from the eigenvalues sorted from large to small, and the eigenvectors corresponding to the eigenvalues are extracted to serve as the standardized variable matrix. There is a very strong correlation between the extracted eigenvectors and the operating parameters of the building roof equipment and the power operation parameters of photovoltaic power generation. The standardized variable matrix is transformed to obtain the principal component variables. A comprehensive evaluation is performed on the principal component variables to finally determine each principal component variable for the principal components that affect the building roof equipment and photovoltaic power generation.
[0039] Further, the operating parameters monitored by six types of sensors are used to construct m sample matrices with n-dimensional random vectors, as follows:
[0040] The n-dimensional random vector x = (x1, x2, …, x n ) T , and m samples x i = (x i1 , x i2 , …, x in ) T , i = 1, 2, …, m, (m > n)
[0041] This sample matrix is standardized to obtain the standard matrix A, as follows:
[0042]
[0043] Wherein,
[0044] Wherein, S j 2 is the variance of the sample matrix. Solving the correlation coefficient matrix for the standardized matrix A of the constructed operating parameters is used to determine the principal components that potentially affect the building roof equipment and potentially affect photovoltaic power generation, including:
[0045]
[0046] Among them
[0047] Among them, R is the correlation coefficient matrix, and the element r in the correlation matrix is related to the standardized matrix A.
[0048] S103. Perform correlation analysis on the first characteristic parameter and the second characteristic parameter to determine the main components affecting photovoltaic power generation.
[0049] In an embodiment of the present application, correlation analysis is performed on the first characteristic parameter and the second operating parameter through the calculation method of covariance. And correlation analysis is performed on the second characteristic parameter and the second operating parameter through the calculation method of covariance. Based on the analysis results, the main components affecting photovoltaic power generation are determined.
[0050] Specifically, solve the characteristic equation |R - λI n = 0 to obtain n characteristic roots and determine the main components.
[0051] According to to determine the value of k so that the utilization rate of data reaches more than 95%.
[0052] For each λ j , j = 1, 2,..., k, solve the system of equations R b = λ j b to obtain the unit eigenvector
[0053] Convert the standardized index variables to obtain the main components:
[0054]
[0055] Obtain U1 called the first principal component, U2 called the first principal component,..., and U called the nth principal component n .
[0056] Perform comprehensive evaluation on the k main components, specifically including:
[0057] Perform weighted summation on the k main components, that is, obtain the final evaluation value, and the weights become the variance contribution rates of each main component.
[0058] Select the main component U with the largest variance contribution rate of each main component n to obtain the main components affecting building roof equipment and affecting photovoltaic power generation.
[0059] S104. Based on the preset status analysis model and the first feature parameter, obtain the status analysis information corresponding to the building roof equipment. According to the status analysis information, determine the fault warning level corresponding to the building roof equipment, so as to determine the main components affecting the building roof equipment based on the fault warning level.
[0060] In an embodiment of the present application, a graph neural network corresponding to the preset sensor and the building roof equipment is constructed. Based on the graph neural network, the associated faults that generate mutual influence relationships are determined, and the weights between the associated faults are determined; wherein, the nodes in the graph neural network represent the faults corresponding to the building roof equipment, the edges in the graph neural network represent the relationships between the faults, the weights of the edges represent the degree of association between the faults, and the direction of the edges represents the mutual influence relationship between the faults. Based on the first feature parameter, the fault name is determined; based on the fault name, the associated faults, and the weights between the associated faults, the status analysis information corresponding to the building roof equipment is obtained.
[0061] In an embodiment of the present application, based on the fault name, the corresponding warning level is determined. The current fault and the associated faults related to the current fault are respectively determined for the fault level. Based on the fault level, the fault impact degree level is determined. The fault impact degree level is multiplied by the weight to determine the influence degree of the associated fault on the current fault based on the calculation result, so as to determine the status analysis information corresponding to the building roof equipment based on the influence degree.
[0062] Specifically, a graph neural network of the sensor and the building roof equipment is constructed. Among them, the nodes of the graph neural network represent the faults corresponding to the building roof equipment, the edges represent the relationships between the faults, the weights of the edges represent the degree of association between the faults, and the direction of the edges represents the mutual influence relationship of the faults.
[0063] For example, Figure 2 is a schematic diagram of a graph neural network provided by an embodiment of the present application. As Figure 2 shown, the numbers in the figure are the nodes of the graph neural network, the edges represent the relationships between the faults, and the direction of the edges represents the mutual influence relationship of the faults.
[0064] Furthermore, based on the constructed graph neural network, the associated faults that can have a mutual influence relationship on the faults are determined, as well as the weights between the associated faults and the faults. Determine that the fault name corresponds to four warning levels: emergency warning, major warning, minor warning, and general warning, so as to reflect the urgency of the maintenance work that should be carried out when the building roof equipment fails by the warning level. Corresponding the faults and the associated faults to primary faults and secondary faults respectively. The primary faults and secondary faults are respectively determined as primary influence degree and secondary influence degree. By multiplying the primary influence degree and secondary influence degree by the weight respectively, the influence degree of the associated fault on the fault is obtained, and then the status analysis information of the building roof equipment is determined.
[0065] S105. Based on the main components affecting photovoltaic power generation and the main components affecting building roof equipment, operation and maintenance of building roof equipment and photovoltaic power generation are performed.
[0066] In one embodiment of the present application, according to the main components affecting photovoltaic power generation, operation and maintenance work is carried out on photovoltaic equipment. According to the main components that mainly affect building roof equipment, operation and maintenance work is carried out on building roof equipment.
[0067] The embodiment of the present application extracts features and performs correlation analysis on the collected metal roof operation data and photovoltaic power station operation data, and relies on data collection, analysis and processing technologies such as the Internet of Things, artificial intelligence, and data middle platform to achieve all-weather intelligent monitoring, analysis and alarm, intelligent operation and maintenance, etc. In the six months when this method was applied to a certain BIPV project, early prediction and timely processing of faults were achieved, thereby increasing power generation by 20% year-on-year, greatly increasing the return on investment, and achieving good economic benefits.
[0068] Figure 3 A schematic diagram of a building photovoltaic integrated operation and maintenance device based on the industrial Internet provided in an embodiment of the present application. Figure 3 As shown, the building photovoltaic integrated operation and maintenance equipment based on the industrial Internet is characterized in that the equipment includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: obtain a first operating parameter of the building roof equipment based on a preset sensor, and obtain a second operating parameter of photovoltaic power generation through a photovoltaic inverter; perform feature extraction on the first operating parameter and the second operating parameter to obtain a first characteristic parameter that potentially affects the building roof equipment, and obtain a second characteristic parameter that potentially affects the photovoltaic power generation; perform correlation analysis on the first characteristic parameter and the second characteristic parameter to determine the main component that affects the photovoltaic power generation; obtain the state analysis information corresponding to the building roof equipment based on the preset state analysis model and the first characteristic parameter, and determine the main component that affects the building roof equipment based on the state analysis information; perform operation and maintenance processing on the building roof equipment and the photovoltaic power generation based on the main component that affects the photovoltaic power generation and the main component that affects the building roof equipment.
[0069] The embodiments in the present application are all described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the differences between each embodiment and other embodiments are emphasized. In particular, for the embodiments of the apparatus, device, and non-volatile computer storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and reference can be made to the relevant parts of the method embodiments for the relevant parts.
[0070] The above are only the embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the embodiments of the present application. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims
1. A building photovoltaic integration operation and maintenance method based on the industrial Internet, characterized in that The method includes: Obtaining first operation parameters of a building rooftop device based on a preset sensor, and obtaining second operation parameters of photovoltaic power generation through a photovoltaic inverter; Performing feature extraction on the first operation parameters and the second operation parameters to obtain first feature parameters potentially affecting the building rooftop device and second feature parameters potentially affecting the photovoltaic power generation; Performing correlation analysis on the first feature parameters and the second feature parameters to determine the main components affecting the photovoltaic power generation; Based on a preset state analysis model and the first feature parameters, obtaining state analysis information corresponding to the building rooftop device, and based on the state analysis information, determining the main components affecting the building rooftop device; Performing operation and maintenance processing on the building rooftop device and the photovoltaic power generation based on the main components affecting the photovoltaic power generation and the main components affecting the building rooftop device.
2. The method for building photovoltaic integration operation and maintenance based on industrial Internet according to claim 1, characterized in that The performing correlation analysis on the first feature parameters and the second feature parameters to determine the main components affecting the photovoltaic power generation specifically includes: Performing correlation analysis on the first feature parameters and the second operation parameters by means of covariance calculation; and Performing correlation analysis on the second feature parameters and the second operation parameters by means of covariance calculation; Based on the analysis results, determining the main components affecting the photovoltaic power generation.
3. A building photovoltaic integration operation and maintenance method based on industrial Internet according to claim 1, characterized in that, The obtaining state analysis information corresponding to the building rooftop device based on the preset state analysis model and the first feature parameters specifically includes: Constructing a graph neural network corresponding to the preset sensor and the building rooftop device; Based on the graph neural network, determining associated faults having a mutual influence relationship and determining the weights between the associated faults; wherein the nodes in the graph neural network represent the faults corresponding to the building rooftop device, the edges in the graph neural network represent the relationships between the faults, the weights of the edges represent the degree of association between the faults, and the directions of the edges represent the mutual influence relationships between the faults; Based on the first feature parameters, determining the names of the faults; Based on the names of the faults, the associated faults, and the weights between the associated faults, obtaining state analysis information corresponding to the building rooftop device.
4. A building photovoltaic integration operation and maintenance method based on the industrial Internet according to claim 1, characterized in that The obtaining state analysis information corresponding to the building rooftop device based on the names of the faults, the associated faults, and the weights between the associated faults specifically includes: Based on the names of the faults, determining the corresponding warning levels; Determining the fault levels of the current fault and the associated faults related to the current fault respectively; Based on the fault levels, determining the fault impact degree levels; Performing a product calculation on the fault impact degree levels and the weights to determine the influence degree of the associated faults on the current fault based on the calculation results, and based on the influence degree, determining the state analysis information corresponding to the building rooftop device.
5. The method for building photovoltaic integration operation and maintenance based on industrial Internet according to claim 1, wherein, The performing feature extraction on the first operation parameters and the second operation parameters to obtain first feature parameters potentially affecting the building rooftop device and second feature parameters potentially affecting the photovoltaic power generation specifically includes: Classify the first operating parameter and the second operating parameter; By means of principal component analysis, determine the average value corresponding to each type of operating parameter respectively, and determine the standard deviation corresponding to each type of operating parameter respectively; Construct a standardization matrix based on the average value and standard deviation corresponding to each type of operating parameter respectively; Determine the correlation coefficient matrix corresponding to the standardization matrix, obtain the first characteristic parameter potentially affecting the building roof equipment and the second characteristic parameter potentially affecting the photovoltaic power generation based on the correlation coefficient matrix, so as to determine the principal components potentially affecting the building roof equipment and the photovoltaic power generation based on the first characteristic parameter and the second characteristic parameter.
6. The method for building photovoltaic integration operation and maintenance based on industrial Internet according to claim 5, characterized in that, The determination of the correlation coefficient matrix corresponding to the standardization matrix specifically includes: Decompose the standardization matrix to obtain the eigenvalues and eigenvectors corresponding to each principal component respectively; Obtain the correlation coefficient matrix based on the eigenvalues and eigenvectors; Among them, the eigenvector of the principal component is the entire set of variables composed of the principal component variables, and the eigenvalue of the principal component is the covariance between the principal component variables.
7. A building photovoltaic integration operation and maintenance method based on industrial Internet according to claim 6, characterized in that After obtaining the correlation coefficient matrix based on the eigenvalues and eigenvectors, the method further includes: Perform diagonalization processing on the correlation coefficient matrix to obtain a diagonalized matrix; Based on the diagonalized matrix, calculate new eigenvectors, use the new eigenvectors as new principal component variables, and determine the mapping relationships between the first operating parameter and the second operating parameter and the new principal component variables respectively.
8. A building photovoltaic integration operation and maintenance method based on industrial Internet according to claim 7, characterized in that, After using the new eigenvector as the new principal component variable, the method further includes: Sort the new eigenvectors based on the magnitudes of the eigenvalues to determine the number of principal components to be extracted; Extract the new eigenvectors corresponding to the eigenvalues based on the number to serve as a standardized variable matrix; Convert the standardized variable matrix to obtain principal component variables; Perform a comprehensive evaluation on the principal component variables to obtain the principal components affecting the building roof equipment and the photovoltaic power generation.
9. A building photovoltaic integration operation and maintenance method based on industrial Internet according to claim 1, characterized in that, Among the first operating parameters of the building roof equipment obtained based on the preset sensors, the preset sensors include at least one of a vibration sensor, a water level sensor, a laser ranging sensor, a stress sensor, a roof temperature sensor, and a meteorological sensor; The first operating parameter includes at least one of a vibration speed, a water level height, a deformation amount, a roof stress, a roof temperature, an irradiance, and a wind pressure.
10. An integrated building photovoltaic operation and maintenance device based on the industrial Internet, characterized in that, The device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: Obtain the first operating parameter of the building roof equipment based on the preset sensors, and obtain the second operating parameter of the photovoltaic power generation through a photovoltaic inverter; Feature extraction is performed on the first operating parameter and the second operating parameter to obtain a first characteristic parameter that potentially affects the building rooftop equipment, and a second characteristic parameter that potentially affects the photovoltaic power generation; The first characteristic parameter and the second characteristic parameter are subjected to correlation analysis to determine the main components affecting the photovoltaic power generation; Based on a preset state analysis model and the first characteristic parameter, state analysis information corresponding to the building rooftop equipment is obtained, and the main components affecting the building rooftop equipment are determined based on the state analysis information; Based on the main components affecting the photovoltaic power generation and the main components affecting the building rooftop equipment, operation and maintenance processing is performed on the building rooftop equipment and the photovoltaic power generation.